Uptake of Travel Health Services by Community Pharmacies and Patients Following Pharmacist Immunization Scope Expansion in Ontario, Canada
Bibliographic record
Abstract
In December 2016, pharmacists in Ontario, Canada with authorization to administer injections saw an expansion in their scope from a restriction to the influenza vaccination only to now including an additional 13 vaccine-preventable diseases, largely those related to travel. It was uncertain whether this change in scope would see sufficient uptake, or translate to a corresponding expansion in other travel health service offerings from community pharmacies. In October/November 2017 a survey was conducted of all licensed community pharmacists in Ontario, followed by semi-structured interviews with 6 survey respondents in June 2018. A web-based survey of members of the public from a single region of the province was also conducted in September 2018 to assess uptake of expanded vaccination services. Broad variability in uptake of these services was noted, ranging from the dispensing of travel-related medications and vaccinations only through to vaccine administration and prescribing under medical directive; however, uptake was generally at the lower end of this spectrum. This was evidenced by 94% of pharmacists reporting administering fewer than 10 travel vaccinations per month, fewer than 10% of patients reporting receiving a travel vaccine administered by a pharmacist, and a maximum of 30 pharmacies (of nearly 6000 in the province) designated to provide yellow fever vaccinations. Fewer than 1 in 3 pharmacists reported performing some form of pre-travel consultation in their practice, often limited to low-risk cases only. Barriers and facilitators reported were similar for these services as they were for other non-dispensing services, including insufficient time to integrate the service into their workload, perceived lack of knowledge and confidence in travel health, and low patient awareness of these new services available to them through community pharmacies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".